Evapotranspiration and Plant Water Stress Measurements as the Driving Standard for Agricultural Irrigation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 934

Special Issue Editors


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Guest Editor
Research Center LEAF (Linking Landscape, Environment, Agriculture and Food), Associate Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal
Interests: evapotranspiration; plant water requirements; irrigation; deficit irrigation; plant water stress; green infrastructure water management
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Guest Editor
Instituto Superior de Agronomia, Universidade de Lisboa, LEAF, Lisboa, Portugal
Interests: evapotranspiration; transpiration; soil evaporation; sap flow; eddy covariance; water stress diagnosis; stress-indicator interpretation; irrigation scheduling; fluxes of water and heat around vegetation; ecohydrology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Research Center LEAF-Linking Landscape, Environment, Agriculture and Food-Research Center, Associate Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal
Interests: irrigation modeling; climate change; evapotranspiration; irrigation engineering; soil water balance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Water will be focused on several topics that we have selected based on the following rationale:

  1. With irrigation accounting worldwide for about 70% of all water use, the proper modelling of plant water requirements is a critical issue for water management in agriculture, but its accuracy is highly dependent on the availability of quality data.
  2. Improvements to the existing algorithms for estimating ET are based on ET measurements. These measurements, when applied to tall and/or deep-rooted permanent crops, require expensive equipment, sophisticated know-how, and high commitment from researchers.
  3. Furthermore, reliable evapotranspiration (ET) estimates are more challenging for permanent crops, especially when subjected to water stress, i.e., deficit-irrigated or rainfed crops.
  4. Conversely, a water stress diagnosis is necessary for the interpretation and therefore the best use of such water flux measurements.
  5. Ensuring proper irrigation scheduling requires more than a stress indicator that simply reacts to water stress. Instead, an identified threshold or stress function must be established and applied, allowing a transfer in space and time under identical circumstances. This ensures that the meaning of the threshold or stress function remains consistent, making it easier to adapt and manage irrigation schedules effectively.
  6. The analysis of survival mechanisms regarding water stress (hydraulic redistribution) and the derived new concepts around functional root volume are critical to our understanding of plant water use in almost-rainfed stands, mainly in Mediterranean and semi-arid climates. 
  7. We do not yet have sufficient useful data from measuring ET and quantifying and understanding stress levels. More reliable field data, sound interpretations, and the good communication of results are necessary in order to best aid the scientific/engineering community.

Considering these points, we welcome the submission of manuscripts for this Special Issue addressing the following topics: 

  • measurements of evapotranspiration;
  • reference information to be used for modelling and reviewing crop coefficients;
  • assessment of plant water stress;
  • use of crop water stress measurements and threshold establishment to schedule deficit irrigation;
  • resilience to water stress in a plant/soil continuum.

Prof. Dr. Teresa Afonso do Paço
Prof. Dr. Maria Isabel Freire Ribeiro Ferreira
Guest Editors

Prof. Dr. Joao Rolim Lopes
Guest Editor Assistant

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Keywords

  • evapotranspiration
  • irrigation scheduling
  • deficit irrigation
  • water stress indicators
  • ground truth data

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Published Papers (1 paper)

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13 pages, 5756 KiB  
Technical Note
Machine Learning Based Peach Leaf Temperature Prediction Model for Measuring Water Stress
by Heetae Kim, Minyoung Kim, Youngjin Kim, Byounggap Kim, Choungkeun Lee and Jaeseung No
Water 2024, 16(21), 3157; https://doi.org/10.3390/w16213157 - 4 Nov 2024
Viewed by 523
Abstract
When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf [...] Read more.
When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf temperature based on environmental data, without relying on sensors, for calculating CWSI. The data underwent preprocessing to remove outliers and missing values. The number of training data points for each factor was 307,924. After data preprocessing, a Pearson correlation analysis (bivariate correlation coefficient) was conducted to select the training data for model operation. The relationship between leaf temperature and air temperature showed a strong positive correlation of 0.928 (p < 0.01). Solar radiation and relative humidity were also found to have high correlations. However, wind speed and soil moisture tension showed very low correlations with leaf temperature and were excluded from the model operation. The Decision Tree, Random Forest, and Gradient Boosting models were selected, and each model was evaluated using RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Squared Error), and R2 (coefficient of determination). The evaluation results showed that the Gradient Boosting model had a high R2 (0.97) and low RMSE (0.88) and MAE (0.54), making it the most suitable model for predicting leaf temperature. Through the leaf temperature prediction model developed in this study, labor and costs associated with sensors can be reduced, and by applying it to real agricultural settings, it can improve crop quality and enhance the sustainability of agriculture. Full article
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